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• W2911278710 abstract "This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations." @default.
• W2911278710 created "2019-02-21" @default.
• W2911278710 creator A5018754425 @default.
• W2911278710 creator A5062908566 @default.
• W2911278710 date "2019-02-04" @default.
• W2911278710 modified "2023-10-13" @default.
• W2911278710 title "Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO ‐ OFDM systems" @default.
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• W2911278710 doi "https://doi.org/10.4218/etrij.2018-0190" @default.
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